Abstract

The banking sector faces growing challenges in identifying and managing risks due to the complexity of financial transactions and increasing fraud. This research presents a framework that combines multiple agents with deep learning to improve risk prediction in banking. Each agent focuses on specific tasks like cleaning data, selecting important features, and detecting unusual activities, ensuring a detailed risk assessment. A deep learning model is used to analyze large amounts of transaction data and identify patterns that may signal potential risks. Tests on real-world banking data show that this approach is more accurate, faster, and effective than traditional methods. By combining the strengths of multi-agent systems and deep learning, this study offers a reliable and flexible solution to help banks manage and adapt to changing risks more effectively.